With the advances in instance segmentation, semantic mapping evolves into the instance level. A good example is
[1]} that offers object-level semantic mapping by identifying individual objects via a bounding box detection module and an unsupervised geometric segmentation module.
Unlike other dense semantic mapping approaches, Fusion++ [2]} builds a semantic graph-based map, which predicts only object instances and maintains a consistent map via loop closure detection, pose-graph optimization and further refinement.
[3]} presented a framework that achieves instance-aware semantic mapping, and enables novel object discovery.
Recently, panoptic segmentation [4]} attracts a lot of attentions. PanopticFusion [5]} advanced semantic mapping to the level of stuff and things level that classifies static objects, e.g. walls, doors, lanes as stuff classes, and other accountable objects as things classes, e.g. moving vehicles, human and tables. Figure REF  compares semantic segmentation, instance segmentation and panoptic segmentation.
